Organizations are in the constant pursuit of reliable strategies for increasing customer satisfaction. One of these strategies refers to the implementation of the ISO 9001 standard as a quality management system to ensure that products and services meet or surpass customer expectations. However, successful ISO 9001-based systems are possible only if companies count on a series of critical success factors that guarantee successful ISO 9001 implementation. This research aims at developing and validating a survey instrument to evaluate the degree of compliance with ISO 9001 critical success factors among manufacturing organizations. The research is conducted in Mexican manufacturing companies. The survey is designed by considering the quality management principles of the ISO 9001:2015 standard as critical success factors. We conducted an exploratory and a confirmatory factor analysis to identify the structure of the survey and confirm its validity. Our findings confirm that the survey instrument comprises four constructs, three of them associated with the quality principles of the ISO 9001 standard, and one associated with the benefits of said standard. In conclusion, the proposed survey is a valid, useful tool to assess ISO 9001 implementation in manufacturing companies and similar organizations.

This paper aims to provide new insights for organizations that have decided to implement a Quality Management System (QMS) based on the ISO 9001 standard as a strategy for continuous improvement and gaining competitive advantage. This objective is achieved through statistically modeling the causal relationships between the Critical Success Factors (CSFs) or enablers of ISO 9001. A statistically validated questionnaire was used to collect information mainly from quality managers working in manufacturing companies located in Mexico that have had an ISO 9001 certificate. Some hypotheses for relationships between ISO CSFs are developed theoretically first and empirically tested later using confirmatory factor analysis and the structural equation modeling technique. The main theoretical contribution of our findings is based on the organizational leadership theory by demonstrating that relationships between ISO CSFs and benefits have been statistically significant and highlighting that for a successful implementation of a QMS based on this standard, a real commitment of top management is undoubtedly required to initialize and give support to a process that activates the remaining enablers leading their organizations to high performance and competitiveness. Our findings have important implications for quality professionals and business managers by identifying actions or activities where to allocate the available resources to increase the likelihood of successfully adopting the standard and thus getting the associated benefits. Unlike other models, our enablers conform by grouping information structured based on the Quality Management Principles (QMPs) defined in the standard. Future research could consider the point of view of all employees involved in the QMS and variations in organizational size or industry sector for their potential effect on effectively implementing a QMS based on ISO 9001. Moreover, further research could also be undertaken to confirm the findings of this study in other countries.

The purpose of this research is to provide a current situation of the ISO 9001 standard in the manufacturing sector in Mexico identifying its critical success factors, barriers and benefits of its implementation. A survey with 55 items in a five-point Likert scale was used mainly focused on quality managers and provided by e-mail and the social network LinkedIn obtaining 172 complete responses. The ISO 9001 standard is one of the initial strategies to manage quality, however, results exhibit some barriers hindering its implementation. According to the seven quality principles for the implementation of the standard established by the International Organization for Standardization, results exposed that the leadership and customer focus are the main factors considerated for a satisfactory adoption of the standard in the manufacturing sector in Mexico helping this industry to achieve some benefits.

Background/Objectives: Efficient management of intensive care unit (ICU) resources is a critical challenge for modern healthcare systems, which must balance high-quality patient care with operational and financial performance. ICU length of stay (LOS) is a key metric of clinical complexity and hospital efficiency. However, traditional methods for predicting LOS often fail to capture the complex, nonlinear interactions among physiological, demographic, and treatment-related variables. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for enhancing predictive accuracy and supporting data-driven decision-making. Methods: This study presents a systematic review and meta-analysis of ML and DL approaches for predicting ICU LOS in adult patients. Following PRISMA guidelines, eight scientific databases were searched, yielding 33 eligible studies published between 2015 and 2025. Results: Mixed medical–surgical ICUs were the most common setting (51.5%), and 45.5% of datasets were sourced from public repositories. Most studies (19/33) focused on binary classification of prolonged stays, although thresholds ranged from >48 h to ≥14 days. The pooled results from ten studies yielded an AUROC of 0.9005 (95% CI: 0.8890–0.9121), indicating strong predictive capability across diverse clinical contexts. Subgroup analyses showed comparable performance between specialized surgical and general ICUs. Conclusions: These findings suggest that AI-driven LOS prediction models exhibit strong discriminatory power for ICU LOS prediction, supporting hospital capacity planning. However, to translate this into reliable clinical support, the methodological heterogeneity, scarcity of external validation, and near absence of calibration reporting identified in this review need to be addressed.

Introduction. Cardiovascular disease represents a substantial global health burden, and delays in diagnosis can further compromise clinical outcomes. Objective. This review aimed to evaluate the diagnostic effectiveness of artificial intelligence models across major cardiovascular conditions, with a specific focus on coronary artery disease, acute coronary syndromes and myocardial infarction, and heart failure. Method. Following PRISMA guidelines, we conducted a systematic review and applied meta-analytical techniques to pool measures of diagnostic performance. Random effects models with restricted maximum likelihood and Hartung–Knapp adjustments were used to calculate summary estimates, and analyses were stratified by cardiovascular condition. Only effects with variance (reported 95% CI or derivable denominators) were pooled. Areas under the receiver operating characteristic curve without variance were synthesized narratively. Results. Out of 39,383 records, thirty-five studies were eligible. The pooled area under the receiver operating characteristic curve was 0.823 (95% confidence interval 0.754–0.892). Heterogeneity was considerable (I² = 98.4%), and the 95% prediction interval was 0.59 to 1. However, 9 of 35 included studies reported external/independent validation. The pooled estimate therefore represents an overall descriptive average, reflecting the expected range of diagnostic performance rather than a single universal value. Stratified results showed pooled values of 0.88 for acute coronary syndromes and myocardial infarction, 0.87 for heart failure, and 0.86 for coronary artery disease. Sensitivity often reached 0.90 or higher in electrocardiography deep learning models, while specificity remained variable. Conclusion. Artificial intelligence models demonstrated promising discrimination across cardiovascular conditions, with variable specificity and predictive value, indicating that clinical utility requires external validation and local calibration.

Introducción: la vulnerabilidad de los migrantes al no encontrarse en su entorno habitual crea un rango de necesidades elementales que hay que solventar, como lo es la seguridad, vivienda, cultura y salud. Metodología: la investigación radica en un enfoque cualitativo donde la información se obtuvo de una revisión de 47 artículos científicos, se realizó una reflexión empírico-racional de alcance exploratorio-analítico para definir y reconocer las intervenciones sociales. Resultados: se reflejan dos problemáticas: (1) el cumplimiento de los servicios de salud por parte de las instituciones públicas y (2) la empatía con el migrante, donde se relacionan los aspectos de su origen y cultura. Discusión: las intervenciones que se apliquen podrán ser exitosas si se comprenden los factores sociales, económicos y políticos que intervienen en la salud de los migrantes. Además, mejorará la coordinación, calidad y disponibilidad de los servicios médicos. Conclusiones: los enfoques culturales son relevantes para abordar las barreras de acceso a la atención médica, así como la prevención de la salud mental o enfermedades de transmisión en los migrantes.